Analogue correction method of errors and its application to numerical weather prediction

In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can effectively reduce model errors by combining the statistical analogue method with the dynamical model together in order that the information of plenty of historical data is utilized in the current complicated NWP model, Furthermore, in the ACE, the differences of the similarities between different historical analogues and the current initial state are considered as the weights for estimating model errors. The results of daily, decad and monthly prediction experiments on a complicated T63 atmospheric model show that the performance of the ACE by correcting model errors based on the estimation of the errors of 4 historical analogue predictions is not only better than that of the scheme of only introducing the correction of the errors of every single analogue prediction, but is also better than that of the T63 model.

Analogue Correction Method of Errors by Combining Statistical and Dynamical Methods

Based on the atmospheric analogy principle, the inverse problem that the information of historical analogue data is utilized to estimate model errors is put forward and a method of analogue correction of errors （ACE） of model is developed in this paper. The ACE can combine effectively statistical and dynamical methods, and need not change the current numerical prediction models. The new method not only adequately utilizes dynamical achievements but also can reasonably absorb the information of a great many analogues in historical data in order to reduce model errors and improve forecast skill. Purthermore, the ACE may identify specific historical data for the solution of the inverse problem in terms of the particularity of current forecast. The qualitative analyses show that the ACE is theoretically equivalent to the principle of the previous analogue-dynamical model, but need not rebuild the complicated analogue-deviation model, so has better feasibility and operational foreground. Moreover, under the ideal situations, when numerical models or historical analogues are perfect, the forecast of the ACE would transform into the forecast of dynamical or statistical method, respectively.

Analogue-Dynamical Prediction of Monsoon Precipitation in Northeast China Based on Dynamic and Optimal Configuration of Multiple Predictors

Based on the National Climate Center （NCC） of China operational seasonal prediction model results for the period 1983-2009 and the US National Weather Service Climate Prediction Center merged analysis of precipitation in the same period, together with the 74 circulation indices of NCC Climate System Diagnostic Division and 40 climate indices of NOAA of US during 1951 2009, an analogue-dynamical technique for objective and quantitative prediction of monsoon precipitation in Northeast China is proposed and implemented. Useful information is extracted from the historical data to estimate the model forecast errors. Dominant predictors and the predictors that exhibit evolving analogues are identified through cross validating the anomaly correlation coefficients （ACC） among single predictors, meanwhile with reference of the results from the dynamic analogue bias correction using four analogue samples. Next, an optimal configuration of multiple predictors is set up and compared with historical optimal multi-predictor configurations and then dynamically adjusted. Finally, the model errors are evaluated and utilized to correct the NCC operational seasonal prediction model results, and the forecast of monsoon precipitation is obtained at last. The independent sample validation shows that this technique has effectively improved the monsoon precipitation prediction skill during 2005 -2009. This study demonstrates that the analogue-dynamical approach is feasible in operational prediction of monsoon precipitation.

Extracting predictable components and forecasting techniques in extended-range numerical weather prediction

This paper refers to the CNOP-related algorithms and formulates the practical method and forecast techniques of extracting predictable components in a numerical model for predictable components on extended-range scales.Model variables are divided into predictable components and unpredictable chaotic components from the angle of model prediction error growth.The predictable components are defined as those with a slow error growth at a given range.A targeted numerical model for predictable components is established based on the operational dynamical extended-range forecast(DERF)model of the National Climate Center.At the same time,useful information in historical data are combined to find the fields for predictable components in the numerical model that are similar to those for the predictable components in historical data,reducing the variable dimensions in a similar judgment process and further correcting prediction errors of predictable components.Historical data is used to obtain the expected value and variance of the chaotic components through the ensemble forecast method.The numerical experiment results show that this method can effectively improve the forecast skill of the atmospheric circulation field in the 10–30 days extended-range numerical model and has good prospects for operational applications.